Complex event analytics systems continuously evaluate massive workloads of pattern queries on high volume event streams to detect and extract complex events of interest to the application. Such time-critical stream-based applications range from real-time fraud detection to personalized health monitoring. Achieving near real-time system responsiveness when processing these workloads composed of complex event pattern queries is their main challenge. In this article, we first review several unique

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... optimization opportunities that we have identified for complex event analytics. We then introduce a family of optimization strategies that consider event correlation over time to maximally leverage sharing opportunities in event pattern detection and aggregation. Lastly, we describe the event-stream transaction model we designed to ensure high performance shared pattern processing on modern multi-core architectures.